Evolving classifiers and in particular evolving (neuro-)fuzzy classifiers (EFCs) are a prominent field of research on incremental learning algorithms, as they i.) are able to self-evolve their structure from newly incoming (stream) samples, allowing them to incrementally expand their knowledge to new systems states [1]} [2]}, newly arising classes, novelty content in stream samples [3]}, drifts [4]} etc.; ii.) are able to address uncertainty in the data (e.g., due to measurement noise) in a natural way through the concepts of granules [5]} [6]}, typically realized through fuzzy sets and rules [7]}; and iii.) allow some sort of interpretability and thus insights into the process due to the extraction of linguistically readable IF-THEN rules, which also offers good explainability of model classification outputs [8]}.
The first aspect is necessary to properly deal with the increasing dynamics and speed of today's data streams.
The latter two aspects are not so easily and directly addressable with other types of ML models such as extreme learning machines, (deep) neural networks, support vector machines etc., and specific sophisticated techniques have to be integrated to make such ML models interpretable and their outputs explainable [9]}.
